Past, Present and Future
2024-11-07
“Multilevel” structure:
“Multilevel” models:
Prelude
The development of multi-level approaches in epidemiologic research may facilitate research which elucidates the independent and joint effects of individual and environmental factors on health behaviors and health outcomes.
Social context and ‘population perspective’ has been forgotten.
Our work with individuals has been useful and productive, but this approach alone clearly will not lead to an effective program of health promotion and disease prevention. A new inititive focusing on the environments in which we live much now become a priority for us all.
Social context a crucial element of conceptual models for ‘social determinants of health’
Place-based comparisons of health are revealing (Villermé, Farr, Graunt, Snow, DuBois, many others)
Communities inherently reflect social dynamics.
Host-Agent-Environment (physical and social).
“Population perspective”, contra biomedical individualism.
Act 1: The Big Idea
Multilevel analyses showed that a measure of collective efficacy yields a high between-neighborhood reliability and is negatively associated with variations in violence, when individual-level characteristics, measurement error, and prior violence are controlled. Associations of concentrated disadvantage and residential instability with violence are largely mediated by collective efficacy.
Focus on ‘simultaneous’ effects:
By incorporating multiple levels of determination in the study of individual outcomes, multilevel analysis allows for the effects of macro- and micro-level variables as well as their interactions
Potential:
Multilevel analysis is one way to begin to restore a population or societal dimension to epidemiologic research
The ‘Big Idea’:
The big idea is that what matters in determining mortality and health in a society is less the overall wealth of that society and more how evenly wealth is distributed.
In 23 of the 25 studies we identified, researchers reported a statistically significant association between at least one neighbourhood measure of socioeconomic status and health, controlling for individual socioeconomic status.
…serve the purpose of identifying types of geographical areas where traditional public health interventions, aimed at individual risk reduction, may best be targeted.
Traditional measures of association such as odds ratios thus provide an incomplete epidemiological basis for decision making in public health interventions.
Act 2: A Crisis of Confidence?
~4600 families in high poverty randomized to housing vouchers.
Generated large differences in exposure to high-poverty neighborhoods.
5-year follow-up (2003):
Many limitations.
The recent and enthusiastic adoption of the multilevel model for neighborhood effects research appears to be a case of statisticism, a term used to describe an almost ritualistic appeal to significance testing and both sampling and measurement error when they are not the problem
The recent and enthusiastic adoption of the multilevel model for neighborhood effects research appears to be a case of statisticism, a term used to describe an almost ritualistic appeal to significance testing and both sampling and measurement error when they are not the problem
Evidence for the income inequality/health link was “slowly dissipating”
Multilevel studies inconsistent in US.
Weak evidence from Europe and Asia.
Individual-level controls matter.
Fixed effects: No.
Random effects: Yes!
“it is not clear how much we are learning, or whether such lessons are improving population health…experimental evidence of neighborhood effects is mixed, and observational studies too often report mere correlations, side-stepping critical effect identification issues. Since epidemiologists have long known that disadvantaged environments are not healthy, the utility of studies that do not face the difficult methodological challenges is questionable”
Act 3: A Way Forward?
Greater focus on credible study designs.
Utilizing longitudinal data to focus on changes in exposure
Weighting methods to deal with observables and post-exposure covariates
Extensions to mediation
All fit within the scope of multilevel design and analysis
Defining assumptions for causal effects of contextual exposures
Time-varying exposures and confounding
Conditional vs. marginal effects
These findings provide little support for social causation as the explanation for associations between neighborhood characteristics and health outcomes.
…the ITT estimate…can successfully measure the effects of the policy initiative, but is not well suited to capturing neighborhood effects.
Random assignment of families to different MTO mobility groups…generates large differences in average neighborhood trajectories
Nonexperimental analyses of the type conducted by CM reintroduce all of the selection bias problems that MTO was designed to overcome.
Time-varying covariates controlled using IPTW, exposure effects estimated using MSMs.
Can replicate MTO findings.
Found significant lagged effect of living in concentrated disadvantage compared with advantage at wave 1
… suggests that the duration of exposure to better environments during childhood is an important determinant
…incoming refugees were assigned to neighborhoods with varying levels of disadvantage throughout the country
…As a result, this study attempts to address the challenges of selective migration present in existing studies on neighborhood outcomes.
…refugees who were assigned to more disadvantaged neighborhoods were more likely to develop hypertension, hyperlipidemia, diabetes, and MI in subsequent decades.
Effect sizes were small, representing a 2% increase from baseline rates for each condition…
Recent review of ‘causal analyses’ of neighborhood effects.
Much more mixed.
Evidence of selection and confounding.
Lots of heterogeneity.
Stronger evidence for children than adults.
\[y_{ij} = \beta \gamma_{j} + \mu_{0j} + e_{0ij}\]
\[[\mu_{0j}] \sim N(0, \sigma^{2}_{strata})]\]
\[[e_{0ij}] \sim N(0, \sigma^{2}_{e_{0}})\]